Spanish National Research Council

About: Spanish National Research Council is a government organization based out in Madrid, Spain. It is known for research contribution in the topics: Population & Galaxy. The organization has 79563 authors who have published 220470 publications receiving 7698991 citations. The organization is also known as: CSIC & Consejo Superior de Investigaciones Científicas.

Performance of Drought Indices for Ecological, Agricultural, and Hydrological Applications

Abstract: In this study, the authors provide a global assessment of the performance of different drought indices for monitoring drought impacts on several hydrological, agricultural, and ecological response variables. For this purpose, they compare the performance of several drought indices [the standardized precipitation index (SPI); four versions of the Palmer drought severity index (PDSI); and the standardized precipitation evapotranspiration index (SPEI)] to predict changes in streamflow, soil moisture, forest growth, and crop yield. The authors found a superior capability of the SPEI and the SPI drought indices, which are calculated on different time scales than the Palmer indices to capture the drought impacts on the aforementioned hydrological, agricultural, and ecological variables. They detected small differences in the comparative performance of the SPI and the SPEI indices, but the SPEI was the drought index that best captured the responses of the assessed variables to drought in summer, the seas...

Mutation Frequencies and Antibiotic Resistance

Abstract: Antibiotic resistance can be achieved by horizontal acquisition of resistance genes (carried by plasmids or transposons), by recombination of foreign DNA into the chromosome, or by mutations in different chromosomal loci ([15][1]). In studies of molecular evolutionary biology, the term mutation rate

Atomic force and scanning tunneling microscopy imaging of graphene nanosheets derived from graphite oxide.

Abstract: Graphene nanosheets produced in the form of stable aqueous dispersions by chemical reduction of graphene oxide and deposited onto graphite substrates have been investigated by atomic force and scanning tunneling microscopy (AFM/STM). The chemically reduced graphene oxide nanosheets were hardly distinguishable from their unreduced counterparts in the topographic AFM images. However, they could be readily discriminated through phase imaging in the attractive regime of tapping-mode AFM, probably because of differences in hydrophilicity arising from their distinct oxygen contents. The chemically reduced nanosheets displayed a smoothly undulated, globular morphology on the nanometer scale, with typical vertical variations in the subnanometer range and lateral feature sizes of ∼5−10 nm. Such morphology was attributed to be the result of significant structural disorder in the carbon skeleton, which originates during the strong oxidation that leads to graphene oxide and remains after chemical reduction. Direct ev...

A hierarchical unsupervised growing neural network for clustering gene expression patterns

Abstract: Motivation: We describe a new approach to the analysis of gene expression data coming from DNA array experiments, using an unsupervised neural network. DNA array technologies allow monitoring thousands of genes rapidly and efficiently. One of the interests of these studies is the search for correlated gene expression patterns, and this is usually achieved by clustering them. The Self-Organising Tree Algorithm, (SOTA) (Dopazo,J. and Carazo,J.M. (1997) J. Mol. Evol., 44, 226‐233), is a neural network that grows adopting the topology of a binary tree. The result of the algorithm is a hierarchical cluster obtained with the accuracy and robustness of a neural network. Results: SOTA clustering confers several advantages over classical hierarchical clustering methods. SOTA is a divisive method: the clustering process is performed from top to bottom, i.e. the highest hierarchical levels are resolved before going to the details of the lowest levels. The growing can be stopped at the desired hierarchical level. Moreover, a criterion to stop the growing of the tree, based on the approximate distribution of probability obtained by randomisation of the original data set, is provided. By means of this criterion, a statistical support for the definition of clusters is proposed. In addition, obtaining average gene expression patterns is a built-in feature of the algorithm. Different neurons defining the different hierarchical levels represent the averages of the gene expression patterns contained in the clusters. Since SOTA runtimes are approximately linear with the number of items to be classified, it is especially suitable for dealing with huge amounts of data. The method proposed is very general and applies to any data providing that they can be coded as a series of numbers and that a computable measure of similarity between data items can be used. Availability: A server running the program can be found at: http://bioinfo.cnio.es/sotarray